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Tree species information is crucial for accurate forest parameter estimation. Small footprint high density multireturn Light Detection and Ranging (LiDAR) data contain a large amount of structural details for modelling and thus distinguishing individual tree species. To fully exploit the potential of these data, we propose a data-driven tree species classification approach based on a volumetric analysis of single-tree-point-cloud that extracts features that are able to characterize both the internal and the external crown structure. The method captures the spatial distribution of the LiDAR points within the crown by generating a feature vector representing the threedimensional (3D) crown information. Each element in the feature vector uniquely corresponds to an Elementary Quantization Volume (EQV) of the crown. Three strategies have been defined to generate unique EQVs that model different representations of the crown components. The classification is performed by using a Support Vector Machines (C-SVM) classifier using the histogram intersection kernel that has the enhanced ability to give maximum preference to the key features in high dimensional feature space. All the experiments were performed on a set of 200 trees belonging to Norway Spruce, European Larch, Swiss Pine, and Silver Fir (i.e., 50 trees per species). The classifier is trained using 120 trees and tested on an independent set of 80 trees. The proposed method outperforms the classification performance of the state-of-the-art method used for comparison.
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